Social Interaction-Aware Dynamical Models and Decision-Making for Autonomous Vehicles

Article Engineering, Electrical & Electronic

Interaction-Aware Decision-Making for Autonomous Vehicles

Yongli Chen et al.

Summary: This article proposes an interaction-aware decision-making approach for autonomous vehicles, which models the interaction between vehicles and pedestrians, and balances safety and efficiency through optimization.

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION (2023)

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Article Engineering, Civil

Vehicle Trajectory Prediction in Connected Environments via Heterogeneous Context-Aware Graph Convolutional Networks

Yuhuan Lu et al.

Summary: Accurate trajectory prediction of surrounding vehicles is crucial for the sustainability and safety of connected and autonomous vehicles. This study proposes a novel Heterogeneous Context-Aware Graph Convolutional Networks that extracts hidden contexts from individual historical trajectories, driving scenes, and inter-vehicle interactions. The model achieves high prediction accuracy and stability on real-world datasets.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2023)

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Article Computer Science, Artificial Intelligence

Interaction-Aware Decision-Making for Automated Vehicles Using Social Value Orientation

Luca Crosato et al.

Summary: This paper proposes a framework based on Social Value Orientation and Deep Reinforcement Learning (DRL) for decision-making in the presence of pedestrians. The framework trains decision-making policies with different driving styles using state-of-the-art DRL algorithms in a simulated environment. It also introduces a computationally-efficient pedestrian model suitable for DRL training.

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES (2023)

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Article Ergonomics

Deceleration parameters as implicit communication signals for pedestrians' crossing decisions and estimations of automated vehicle behaviour

Kai Tian et al.

Summary: This study examines the impact of implicit communication signals on pedestrians' subjective estimations of approaching vehicle behavior and their crossing decisions. The findings show that deceleration behavior plays a crucial role in pedestrians' estimations and decisions, and that vehicle speed and traffic gap also influence their evaluations.

ACCIDENT ANALYSIS AND PREVENTION (2023)

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Article Multidisciplinary Sciences

Explaining human interactions on the road by large-scale integration of computational psychological theory

Gustav Markkula et al.

Summary: When humans share space in road traffic, it is important to consider their communicative and interactive capabilities in modeling automated vehicles. Computational theories exist in psychology for studying human behavior, and these theories can be integrated into a computational framework to model human road user interaction. By using Bayesian perception, theory of mind, behavioral game theory, long-term valuation, and evidence accumulation decision-making, the model can explain and predict naturalistic driver-pedestrian road-crossing interactions. This study demonstrates the value of these theories and emphasizes the complexity of human interaction in road traffic for the development and testing of vehicle automation.

PNAS NEXUS (2023)

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Review Computer Science, Information Systems

Driver Behavior Modeling Toward Autonomous Vehicles: Comprehensive Review

Natnael M. M. Negash et al.

Summary: Driver behavior models are used in self-coaching, accident prevention studies, and developing driver-assisting systems. Driver behavior recognition has revolutionized autonomous vehicles and traffic management studies. This survey reviews the different driver behavior models and modeling approaches.

IEEE ACCESS (2023)

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Article Computer Science, Information Systems

Driver-Pedestrian Interactions at Unsignalized Crossings Are Not in Line With the Nash Equilibrium

Amir Hossein Kalantari et al.

Summary: Recent developments in vehicle automation have led to the need for simulating human-robot interactions in road traffic. This study compared four game-theoretic models and found that a behavioral game-theoretic model outperformed other models in predicting the outcome of vehicle-pedestrian interactions.

IEEE ACCESS (2023)

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Article Behavioral Sciences

External Human-Machine Interfaces Can Be Misleading: An Examination of Trust Development and Misuse in a CAVE-Based Pedestrian Simulation Environment

Anees Ahamed Kaleefathullah et al.

Summary: This study investigated pedestrians' misuse of an automated vehicle equipped with an external human-machine interface (eHMI). The results showed that eHMI can cause pedestrians to rely too much on it and ignore the vehicle's intrinsic cues. Therefore, it is necessary to educate people about the capabilities of eHMI.

HUMAN FACTORS (2022)

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Article Engineering, Civil

Dynamic Driving Risk Potential Field Model Under the Connected and Automated Vehicles Environment and Its Application in Car-Following Modeling

Linheng Li et al.

Summary: This paper proposes a new dynamic driving risk potential field model that considers the dynamic effect of a vehicle's acceleration and steering angle in the connected and automated vehicles (CAVs) environment. The simulation results show that the model accurately describes car-following behavior and outperforms other classical models in frequent oscillation phases. Additionally, the model is successfully used to deduce safety conditions for vehicle lane-changing.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Article Engineering, Civil

Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

Szilard Aradi

Summary: Academic research in the field of autonomous vehicles has gained popularity in recent years, covering various topics such as sensor technologies, communication, safety, decision making, and control. Artificial Intelligence and Machine Learning methods have become integral parts of this research. Motion planning, with a focus on strategic decision-making, trajectory planning, and control, has also been studied. This article specifically explores Deep Reinforcement Learning (DRL) as a field within Machine Learning. The paper provides insights into hierarchical motion planning and the basics of DRL, including environment modeling, state representation, perception models, reward mechanisms, and neural network implementation. It also discusses vehicle models, simulation possibilities, and computational requirements. The paper surveys state-of-the-art solutions, categorized by different tasks and levels of autonomous driving, such as car-following, lane-keeping, trajectory following, merging, and driving in dense traffic. Lastly, it raises open questions and future challenges.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Article Transportation Science & Technology

A context-aware pedestrian trajectory prediction framework for automated vehicles

Arash Kalatian et al.

Summary: Studying pedestrian behavior in automated urban environments is crucial, and current datasets lack pedestrian perspectives. Virtual reality data can be used as a complementary resource to measure pedestrian behavior in specific conditions. In this study, a context-aware pedestrian trajectory prediction framework is proposed, incorporating pedestrian head orientations and distance to vehicles to predict future pedestrian trajectories.

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2022)

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Article Psychology, Applied

The effect of drivers' eye contact on pedestrians' perceived safety

V Onkhar et al.

Summary: This research investigates the impact of eye contact between drivers and pedestrians on perceived safety while crossing the road. The study reveals that eye contact significantly increases pedestrians' sense of safety, especially when initiated or terminated at specific times. These findings challenge the notion that eye contact is dispensable in road interactions.

TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR (2022)

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Article Ergonomics

Modelling the pedestrian dilemma zone at uncontrolled midblock sections

Digvijay S. Pawar et al.

Summary: This study models the dilemma zone for pedestrians crossing high-speed roads and analyzes the factors influencing their gap acceptance behavior. The findings provide insights for pedestrians to judge safe gaps and reduce the risk of pedestrian-vehicle crashes.

JOURNAL OF SAFETY RESEARCH (2022)

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Article Ergonomics

Learning to interpret novel eHMI: The effect of vehicle kinematics and eHMI familiarity on pedestrian' crossing behavior

Yee Mun Lee et al.

Summary: This study investigates the impact of vehicle kinematic information and external Human Machine Interface (eHMI) designs on pedestrian crossing decisions. The results show that using flashing headlights as a yielding message can lead pedestrians to cross the road earlier during vehicle deceleration. However, the visibility of a novel slow pulsing light band only has a slight effect on crossing initiation time, and message familiarity also plays a role in pedestrian decisions.

JOURNAL OF SAFETY RESEARCH (2022)

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Article Engineering, Civil

Using Graph-Theoretic Machine Learning to Predict Human Driver Behavior

Rohan Chandra et al.

Summary: Research shows that autonomous vehicles can be socially aware if there is a mechanism to understand human driver behavior. A new approach using machine learning to predict human driver behavior is presented, which extracts driver behavior features and creates a computational mapping between vehicle trajectories and driver behaviors. The method is proven to be robust, general, and applicable to various autonomous navigation scenarios, with evaluations conducted on real-world traffic datasets and simulations.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Article Engineering, Civil

Interpretable End-to-End Urban Autonomous Driving With Latent Deep Reinforcement Learning

Jianyu Chen et al.

Summary: This article introduces an interpretable deep reinforcement learning method for end-to-end autonomous driving, which utilizes a sequential latent environment model for handling complex urban scenarios and significantly reducing the sample complexity of reinforcement learning. Comparative tests in a realistic driving simulator demonstrate that the method outperforms many baseline models including DQN, DDPG, TD3, and SAC in crowded urban environments.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Article Computer Science, Artificial Intelligence

Agent-Based Modeling for Predicting Pedestrian Trajectories Around an Autonomous Vehicle

Manon Prédhumeau et al.

JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH (2022)

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Article Computer Science, Information Systems

Imitation learning based decision-making for autonomous vehicle control at traffic roundabouts

Weichao Wang et al.

Summary: This study presents an imitation learning-based decision making framework that utilizes deep policy networks to provide optimal timing decisions for entering a roundabout, aiming to improve decision-making in driving assistance systems.

MULTIMEDIA TOOLS AND APPLICATIONS (2022)

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Article Robotics

Deep Reinforcement Learning Based Game-Theoretic Decision-Making for Autonomous Vehicles

Mingfeng Yuan et al.

Summary: This study combines game-theoretic decision-making with deep reinforcement learning to enable vehicles to make decisions at unsignalized intersections using 2D Lidar for environmental observations. The inclusion of multiple vehicles and their driving behaviors in the model provides a novel approach to improve the safety and decision-making of self-driving cars.

IEEE ROBOTICS AND AUTOMATION LETTERS (2022)

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Article Multidisciplinary Sciences

Autonomous driving using imitation learning with look ahead point for semi structured environments

Joonwoo Ahn et al.

Summary: Semi-structured environments pose challenges for autonomous driving. Existing methods for avoiding obstacles in real-time are problematic, and localization data is inaccurate. This study proposes a method that uses vision and deep learning to guide the vehicle towards drivable areas, and introduces a more accurate data aggregation algorithm for dangerous situations.

SCIENTIFIC REPORTS (2022)

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Review Engineering, Civil

A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving

Di Feng et al.

Summary: This paper provides a review and comparative study of existing probabilistic object detection methods for autonomous driving applications. It aims to address the issue of uncertainty estimation in deep object detection. The paper presents an overview of practical uncertainty estimation methods in deep learning, surveys existing methods and evaluation metrics, and conducts a comparative study based on an image detector and three public autonomous driving datasets. The remaining challenges and future works are also discussed.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Article Computer Science, Artificial Intelligence

Driver-Pedestrian Perceptual Models Demonstrate Coupling: Implications for Vehicle Automation

Joshua E. Domeyer et al.

Summary: By using joint activity theory, this study explores how drivers and pedestrians balance and negotiate competing risk and velocity goals during movement. Simulation-based inference is used to estimate parameters of coupled driver and pedestrian perceptual models. It is found that dynamic risk and velocity parameters predict safety, efficiency, and fairness outcomes, providing guidance for vehicle automation.

IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS (2022)

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Article Physics, Multidisciplinary

Defensive or competitive Autonomous Vehicles: Which one interacts safely and efficiently with pedestrians?

Hong Zhu et al.

Summary: The emergence of Autonomous Vehicles (AVs) poses unexpected challenges in urban traffic environments, particularly in the conflict between pedestrians and AVs. This study investigates the efficiency and safety performance of unsignalized mid-block crosswalks (UMC) considering the diversities in pedestrian and AV behaviors. The findings suggest that with appropriate parameter settings, competitive AVs are most suitable for UMC in the future.

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2022)

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Article Engineering, Industrial

Explaining unsafe pedestrian road crossing behaviours using a Psychophysics-based gap acceptance model

Kai Tian et al.

Summary: Accidents involving pedestrians are common at unsignalised intersections and mid-block crosswalks. Higher vehicle speed increases the tendency of pedestrians to accept smaller time gaps and engage in unsafe crossings.

SAFETY SCIENCE (2022)

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Article Chemistry, Analytical

How Do Human-Driven Vehicles Avoid Pedestrians in Interactive Environments? A Naturalistic Driving Study

Shulei Sun et al.

Summary: This paper explores the challenges that autonomous vehicles face in shared pedestrian environments and investigates the yielding behavior of human-driven vehicles towards pedestrians through a naturalistic driving study. The findings are crucial for the development of future autonomous vehicles.

SENSORS (2022)

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Article Ergonomics

Impacts of visual and cognitive distractions and time pressure on pedestrian crossing behaviour: A simulator study

Kai Tian et al.

Summary: This study investigated the effects of visual-manual and auditory-cognitive distractions on pedestrian crossing behavior and safety in a controlled environment. The results showed that these two types of distractions impact pedestrian safety differently, with time pressure also leading to an increase in unsafe decisions.

ACCIDENT ANALYSIS AND PREVENTION (2022)

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Article Computer Science, Information Systems

Ghost on the Windshield: Employing a Virtual Human Character to Communicate Pedestrian Acknowledgement and Vehicle Intention

Alexandros Rouchitsas et al.

Summary: Pedestrians base their street-crossing decisions on both vehicle-centric and driver-centric cues, but with the development of autonomous vehicles, there is a need for external human-machine interfaces (eHMIs) to bridge the communication gap. An eHMI concept using a virtual human character (VHC) to communicate pedestrian acknowledgement and vehicle intention was proposed and shown to be effective in ensuring pedestrian safety. The concept utilizes gaze direction for pedestrian acknowledgement and facial expressions for indicating vehicle intention.

INFORMATION (2022)

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Review Computer Science, Information Systems

Review of Decision-Making and Planning Approaches in Automated Driving

Fernando Garrido et al.

Summary: Research on decision-making systems in automated driving has seen a significant increase in recent years, with a focus on different levels such as strategic, tactical, operational, and stability. Supervision can involve a combination of these decision-making levels. Prior reviews either concentrate on a single decision-making level or on specific application environments without differentiating between contexts.

IEEE ACCESS (2022)

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Article Ergonomics

Young and older adult pedestrians' behavior when crossing a street in front of conventional and self-driving cars

Aurelie Dommes et al.

Summary: This study found that pedestrians were more likely to cross the street when AVs stopped in the near lane to let them cross, compared to CVs approaching in the far lane. Risky decisions were more pronounced, especially for older participants, at shorter time gaps, highlighting the potential hazards of introducing AVs into current road traffic.

ACCIDENT ANALYSIS AND PREVENTION (2021)

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Article Psychology, Applied

Modeling the influence of mobile phone use distraction on pedestrian reaction times to green signals: A multilevel mixed-effects parametric survival model

Yan Liu et al.

Summary: Understanding the impact of mobile phone distraction on pedestrian behavior is crucial for safety and performance evaluations. This study found that distractions from mobile phone use significantly lengthen pedestrian reaction times, with males over 40 performing worse in road segment crosswalks with longer distraction durations. Factors such as traffic awareness levels also influence pedestrian reactions.

TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR (2021)

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Review Public, Environmental & Occupational Health

To cross or not to cross? Review and meta-analysis of pedestrian gap acceptance decisions at midblock street crossings

Athanasios Theofilatos et al.

Summary: This study conducted a meta-analysis on the critical factors influencing gap acceptance of pedestrians in midblock street crossings, finding that vehicle speed, gap size, and frequency of attempts significantly impact pedestrians' decision-making, while waiting time has no significant impact on crossing odds.

JOURNAL OF TRANSPORT & HEALTH (2021)

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Article Robotics

Stochastic Dynamic Games in Belief Space

Wilko Schwarting et al.

Summary: Gathering information while interacting with other agents in environments with sensing and motion uncertainty is critical in various domains. Agents must predict others' future actions without communication, account for uncertainty and noise in information gathering, and consider what information their actions reveal. Our algorithm, using local iterative dynamic programming in Gaussian belief space, achieves a runtime polynomial in the number of agents and linear in the planning horizon, providing linear feedback policies for our robot.

IEEE TRANSACTIONS ON ROBOTICS (2021)

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Review Computer Science, Information Systems

A Survey on Deep Learning Based Approaches for Scene Understanding in Autonomous Driving

Zhiyang Guo et al.

Summary: This paper provides a comprehensive survey of deep learning-based approaches for scene understanding in autonomous driving, categorizing them into four work streams and analyzing their characteristics, advantages, and disadvantages. It also summarizes benchmark datasets and evaluation criteria used in the research community, compares the performance of some latest works, and discusses future challenges in the research domain.

ELECTRONICS (2021)

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Article Engineering, Industrial

Automated vehicles that communicate implicitly: examining the use of lateral position within the lane

Anirudh Sripada et al.

Summary: The research suggests that using the lateral deviation of automated vehicles (AVs) can effectively communicate with pedestrians about yielding intentions, with the movement towards pedestrians indicating yielding and away indicating continuing driving. Additionally, the use of turn indicators is seen as an intuitive signal for communicating yielding behavior.

ERGONOMICS (2021)

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Article Engineering, Civil

Driving Behavior Prediction Considering Cognitive Prior and Driving Context

Dong Zhou et al.

Summary: This paper introduces driving behavior modeling in a combination of cognitive and data-driven perspectives, proposes the Predictive-Bi-LSTM-CRF algorithm, and defines a new evaluation metric. Experimental results demonstrate the excellent performance of the algorithm in the prediction task.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

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Article Engineering, Civil

Pedestrian Path Prediction for Autonomous Driving at Un-Signalized Crosswalk Using W/CDM and MSFM

Xi Zhang et al.

Summary: This paper investigates pedestrian path prediction at a 2-second time horizon for autonomous driving using a waiting/crossing decision model and a modified social force model, focusing on conflicts between pedestrians and straight-going vehicles at an un-signalized crosswalk. An integrated method, W/CDM-MSFM, is proposed to accurately predict pedestrian paths by considering factors such as pedestrians' waiting/crossing intentions, conflict avoidance, collision avoidance, and heterogeneous characteristics. The proposed method outperforms existing methods and significantly improves pedestrian safety by accurately predicting pedestrian paths for autonomous driving.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

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Review Chemistry, Analytical

eHMI: Review and Guidelines for Deployment on Autonomous Vehicles

Juan Carmona et al.

Summary: This research aims to investigate the specific issue of information flow between pedestrians and automated vehicles by evaluating recent advances in external human-machine interfaces (eHMI) and analyzing their effectiveness based on pedestrian feedback data. By studying the latest technological developments, it provides guidance on the current status and future direction of eHMI systems.

SENSORS (2021)

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Article Computer Science, Information Systems

Computing Systems for Autonomous Driving: State of the Art and Challenges

Liangkai Liu et al.

Summary: The key to the success of current autonomous driving systems lies in making reliable real-time decisions, while the real traffic environment is too complex for these systems to fully grasp. This article introduces state-of-the-art computing systems for autonomous driving and highlights the challenges to be addressed, aiming to draw attention from the industry and inspire further research.

IEEE INTERNET OF THINGS JOURNAL (2021)

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Article Robotics

Socially-Compatible Behavior Design of Autonomous Vehicles With Verification on Real Human Data

Letian Wang et al.

Summary: With the increasing deployment of autonomous vehicles on public roads, designing socially compatible behaviors with other traffic participants becomes crucial. The proposed uncertain-aware integrated prediction and planning framework allows AVs to infer the characteristics of other road users online and generate behaviors optimizing rewards, courtesy, and confidence regarding uncertainties. Research shows significant variations in driving preferences, such as courtesy towards others, among human drivers in different cultures.

IEEE ROBOTICS AND AUTOMATION LETTERS (2021)

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Article Computer Science, Interdisciplinary Applications

Graph neural network and reinforcement learning for multi-agent cooperative control of connected autonomous vehicles

Sikai Chen et al.

Summary: A connected autonomous vehicle (CAV) network is a group of vehicles operating within a specific spatial scope, sharing traffic information and coordinating movements for safety and mobility. The complex nature of CAV networks poses challenges for cooperative control, which can be addressed using a novel deep reinforcement learning-based algorithm that combines graphic convolution neural network with deep Q-network. The proposed algorithm enhances CAV operations by efficiently aggregating information from sensing and connectivity sources to make operative decisions that improve safety and mobility.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2021)

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Article Robotics

Game-Theoretic Planning for Self-Driving Cars in Multivehicle Competitive Scenarios

Mingyu Wang et al.

Summary: This article presents a nonlinear receding horizon game-theoretic planner specifically designed for autonomous cars in competitive scenarios, providing rich game strategies for trajectories through constraints and optimizations on trajectory and bicycle kinematics. The planner iteratively plans trajectories for the ego vehicle and other vehicles, incorporating a sensitivity term for collision avoidance and significantly outperforming a baseline planner in numerical simulations and experiments.

IEEE TRANSACTIONS ON ROBOTICS (2021)

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Article Biochemical Research Methods

Accumulation of continuously time-varying sensory evidence constrains neural and behavioral responses in human collision threat detection

Gustav Markkula et al.

Summary: Evidence accumulation models of decision-making propose that humans accumulate noisy sensory evidence over time up to a decision threshold. We demonstrate that this type of model can describe human behavior well not only in abstract, semi-static laboratory tasks, but also in a task that is relevant to human movement in the real world. Specifically, we show that a model directly accumulating the continuously time-varying visual looming (optical expansion) of an approaching obstacle explains full probability distributions of when humans can detect this collision threat. Using electroencephalography, we find indications that this type of evidence is accumulated differently in the brain compared to evidence accumulation in previously studied, more abstract tasks.

PLOS COMPUTATIONAL BIOLOGY (2021)

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Article Computer Science, Artificial Intelligence

The Matter of How and When: Comparing Explicit and Implicit Communication Strategies of Automated Vehicles in Bottleneck Scenarios

Michael Rettenmaier et al.

Summary: This study investigates communication strategies for automated vehicles when encountering human drivers. It found that AVs should communicate right-of-way explicitly and implicitly together, with a combined approach rated as highly comprehensible, helping to improve safety and efficiency.

IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS (2021)

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Article Computer Science, Information Systems

Communicating the intention of an automated vehicle to pedestrians: The contributions of eHMI and vehicle behavior

Debargha Dey et al.

Summary: This study investigates the impact of External Human-Machine Interfaces (eHMIs) on pedestrians' understanding of a vehicle's intent and how the movement dynamics of the vehicle affect perception of intent. The results suggest that eHMIs can help pedestrians interpret a vehicle's yielding intention, especially when the vehicle's speed raises doubts about its stopping intention.

IT-INFORMATION TECHNOLOGY (2021)

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Article Engineering, Civil

Pedestrian walking speed at un-signalized midblock crosswalk and its impact on urban street segment performance

Albert Forde et al.

Summary: The study investigates pedestrian walking speeds at un-signalized midblock crosswalks on urban street segments and suggests adjustments to the Urban Street Segment Analysis Chapter in order to consider the impact of pedestrian speeds on vehicle delays.

JOURNAL OF TRAFFIC AND TRANSPORTATION ENGINEERING-ENGLISH EDITION (2021)

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Article Engineering, Civil

Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice

Amir Rasouli et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2020)

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Article Engineering, Electrical & Electronic

Gauss mixture hidden Markov model to characterise and model discretionary lane-change behaviours for autonomous vehicles

Hao Jin et al.

IET INTELLIGENT TRANSPORT SYSTEMS (2020)

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Article Automation & Control Systems

Multi-agent sensitivity enhanced iterative best response: A real-time game theoretic planner for drone racing in 3D environments

Zijian Wang et al.

ROBOTICS AND AUTONOMOUS SYSTEMS (2020)

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Article Computer Science, Information Systems

A driving intention prediction method based on hidden Markov model for autonomous driving

Shiwen Liu et al.

COMPUTER COMMUNICATIONS (2020)

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Article Automation & Control Systems

A Noncooperative Game Approach to Autonomous Racing

Alexander Liniger et al.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2020)

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Article Computer Science, Artificial Intelligence

Driver behavior detection and classification using deep convolutional neural networks

Mohammad Shahverdy et al.

EXPERT SYSTEMS WITH APPLICATIONS (2020)

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Article Multidisciplinary Sciences

Wise or mad crowds? The cognitive mechanisms underlying information cascades

Alan N. Tump et al.

SCIENCE ADVANCES (2020)

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Article Robotics

A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing

Riccardo Spica et al.

IEEE TRANSACTIONS ON ROBOTICS (2020)

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Article Computer Science, Information Systems

Deep reinforcement learning for pedestrian collision avoidance and human-machine cooperative driving

Junxiang Li et al.

INFORMATION SCIENCES (2020)

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Article Physics, Multidisciplinary

Modeling the pedestrian behavior at conflicts with vehicles in multi-lane roundabouts (a cellular automata approach)

Maziyar Layegh et al.

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2020)

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Article Multidisciplinary Sciences

Human-like driving behaviour emerges from a risk-based driver model

Sarvesh Kolekar et al.

NATURE COMMUNICATIONS (2020)

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Article Transportation Science & Technology

Pedestrian Support in Intelligent Transportation Systems: Challenges, Solutions and Open issues

Sara El Hamdani et al.

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2020)

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Proceedings Paper Computer Science, Artificial Intelligence

Implicit Communication of Automated Vehicles in Urban Scenarios: Effects of Pitch and Deceleration on Pedestrian Crossing Behavior

Andre Dietrich et al.

HUMAN SYSTEMS ENGINEERING AND DESIGN II (2020)

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Article Engineering, Electrical & Electronic

Multi-task deep learning with optical flow features for self-driving cars

Yuan Hu et al.

IET INTELLIGENT TRANSPORT SYSTEMS (2020)

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Article Ergonomics

How do drivers overtake pedestrians? Evidence from field test and naturalistic driving data

Alexander Rasch et al.

ACCIDENT ANALYSIS AND PREVENTION (2020)

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Article Ergonomics

Defining interactions: a conceptual framework for understanding interactive behaviour in human and automated road traffic

G. Markkula et al.

THEORETICAL ISSUES IN ERGONOMICS SCIENCE (2020)

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Article Remote Sensing

A Social Force Based Pedestrian Motion Model Considering Multi-Pedestrian Interaction with a Vehicle

Dongfang Yang et al.

ACM TRANSACTIONS ON SPATIAL ALGORITHMS AND SYSTEMS (2020)

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Article Computer Science, Information Systems

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Ekim Yurtsever et al.

IEEE ACCESS (2020)

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Article Robotics

Human-Like Motion Planning Based on Game Theoretic Decision Making

Annemarie Turnwald et al.

INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS (2019)

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Article Behavioral Sciences

External Human-Machine Interfaces on Automated Vehicles: Effects on Pedestrian Crossing Decisions

Koen de Clercq et al.

HUMAN FACTORS (2019)

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Article Computer Science, Interdisciplinary Applications

Game theory modeling for vehicle-pedestrian interactions and simulation based on cellular automata

WenJing Wu et al.

INTERNATIONAL JOURNAL OF MODERN PHYSICS C (2019)

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Article Engineering, Civil

Proxemics and Kinesics in Automated Vehicle-Pedestrian Communication: Representing Ethnographic Observations

Joshua Domeyer et al.

TRANSPORTATION RESEARCH RECORD (2019)

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Article Computer Science, Artificial Intelligence

Pixel and feature level based domain adaptation for object detection in autonomous driving

Yuhu Shan et al.

NEUROCOMPUTING (2019)

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Article Multidisciplinary Sciences

Social behavior for autonomous vehicles

Wilko Schwarting et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)

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Review Chemistry, Analytical

Vehicle-to-Pedestrian Communication for Vulnerable Road Users: Survey, Design Considerations, and Challenges

Parag Sewalkar et al.

SENSORS (2019)

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Article Ergonomics

Gap acceptance probability model for pedestrians at unsignalized mid-block crosswalks based on logistic regression

Jing Zhao et al.

ACCIDENT ANALYSIS AND PREVENTION (2019)

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Article Psychology, Applied

Risk-taking, emotions and socio-cognitive dynamics of pedestrian street-crossing decision-making in the city

Stephanie Coeugnet et al.

TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR (2019)

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Article Ergonomics

Street-crossing workload in young and older pedestrians

Aurelie Dommes

ACCIDENT ANALYSIS AND PREVENTION (2019)

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